code: def test_multiple_distributed_runs(self):
      predictions = [
          torch.tensor(
              [
                  [0.35, 0.25, 0.1, 0.1, 0.2],
                  [0.1, 0.6, 0.1, 0.2, 0.0],
                  [0.1, 0.6, 0.1, 0.2, 0.0],
              ]
          ),
          torch.tensor(
              [
                  [0.1, 0.5, 0.1, 0.2, 0.0],
                  [0.1, 0.2, 0.1, 0.7, 0.0],
                  [0.1, 0.6, 0.1, 0.2, 0.0],
              ]
          ),
      ]
      targets = [torch.tensor([0, 4, 1]), torch.tensor([0, 3, 0])]
      metric_kwargs = {
          "predictions": predictions,
          "gold_labels": targets,
      }
      desired_metrics = {
          "precision": self.desired_precisions,
          "recall": self.desired_recalls,
          "fscore": self.desired_fscores,
      }
      run_distributed_test(
          [-1, -1],
          multiple_runs,
          FBetaMeasure(),
          metric_kwargs,
          desired_metrics,
          exact=False,
      )
  
imports: from typing import Any, Dict, List, Tuple, Union
  import torch
  from sklearn.metrics import precision_recall_fscore_support
  from torch.testing import assert_allclose
  import pytest
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, multi_device, run_distributed_test, global_distributed_metric
  from allennlp.training.metrics import FBetaMeasure
label_desc: Use image node_id 19 for calling the FBetaMeasureTest obj's underlying member method code with example usage: obj.test_multiple_distributed_runs() without return types